mlsanityData to 7 Oct 2026

Towards Personalized Federated Learning via Heterogeneous Model Reassembly

arXiv 2308.08643 ↗First posted 16 Aug 2023cs.LG cs.DCcited by 31

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Abstract (arXiv)

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human intervention. Furthermore, our proposed heterogeneous model reassembly technique mitigates the adverse impact introduced by using public data with different distributions from the client data to a certain extent. Experimental results demonstrate that pFedHR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, pFedHR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 36 of its 44 references that are in the map. First posted on arXiv 16 Aug 2023.

Builds on

36 of 44 references.

References · 36

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24 more
  1. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  3. 2022Git Re-Basin: Merging Models modulo Permutation Symmetriescited by 460
  4. 2019FedMD: Heterogenous Federated Learning via Model Distillationcited by 454
  5. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 349
  6. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  7. 2021Revisiting Model Stitching to Compare Neural Representationscited by 203
  8. 2021FedKD: Communication Efficient Federated Learning via Knowledge Distillationcited by 185
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  13. 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
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  18. 2022Personalized Federated Learning With Graphcited by 29
  19. 2023Stitchable Neural Networkscited by 26
  20. 2023FedFA: Federated Feature Augmentationcited by 22
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 15; 2025: 10; 2026: 4.

31 papers.

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  1. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  2. 2024FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Expertscited by 27
  3. 2024pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learningcited by 21
  4. 2024Federated Model Heterogeneous Matryoshka Representation Learningcited by 21
  5. 2024FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extractioncited by 21
  6. 2024Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modalitycited by 14
  7. 2024Advances in Robust Federated Learning: A Survey with Heterogeneity Considerationscited by 12
  8. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 12
  9. 2023ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learningcited by 9
  10. 2023FedAIoT: A Federated Learning Benchmark for Artificial Intelligence of Thingscited by 8
  11. 2024Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagationcited by 5
  12. 2024Foundation Models in Federated Learning: Assessing Backdoor Vulnerabilitiescited by 3
19 more
  1. 2024pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learningcited by 3
  2. 2024Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosiscited by 2
  3. 2025FedEFM: Federated Endovascular Foundation Model with Unseen Datacited by 2
  4. 2025Communication-Efficient and Personalized Federated Foundation Model Fine-Tuning via Tri-Matrix Adaptationcited by 2
  5. 2025Mosaic: Data-Free Knowledge Distillation via Mixture-of-Experts for Heterogeneous Distributed Environmentscited by 2
  6. 2024FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillationcited by 1
  7. 2024FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Modelscited by 1
  8. 2025PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo Federated Learningcited by 1
  9. 2025FedSKD: Aggregation-free Model-heterogeneous Federated Learning via Multi-dimensional Similarity Knowledge Distillation for Medical Image Classificationcited by 1
  10. 2024GeFL: Model-Agnostic Federated Learning with Generative Modelscited by 0
  11. 2025Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigmscited by 0
  12. 2025Lorica: A Synergistic Fine-Tuning Framework for Advancing Personalized Adversarial Robustnesscited by 0
  13. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0
  14. 2025Dynamic Clustering for Personalized Federated Learning on Heterogeneous Edge Devicescited by 0
  15. 2025Enhancing Graph Neural Networks: A Mutual Learning Approachcited by 0
  16. 2026GAPSL: A Gradient-Aligned Parallel Split Learning over Data-Heterogeneous Edge Computing Systemscited by 0
  17. 2026FedeKD: Energy-Based Gating for Robust Federated Knowledge Distillation under Heterogeneous Settingscited by 0
  18. 2026FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEscited by 0
  19. 2026From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learningcited by 0